The growth of music resources on personal devices and Internet radio has altered the channels for music sales and increased the need for music recommendations. For example, store-based and mail-based CD sales are dropping while music portals for electronic distribution of music (bundled or unbundled) like iTunes, MSN Music, and Amazon are increasing.
To further increase music sales, techniques to generate recommendations are now being used to help consumers find more interesting songs. Many commercial systems such as Amazon.com, Last.fm (http://www.last.fm), and Pandora (http://www.pandora.com) have developed particular approaches for music recommendation. For example, Amazon.com and Last.fm adopt collaborative filtering (CF)-based technologies to generate recommendations. For example, if two users have similar preferences for some music songs, then these techniques assume that these two users tend to have similar preferences for other songs (e.g., song that they may not already own or are aware of). In practice, such user preference is discovered through mining user buying histories. Some other companies such as Pandora utilize content-based technologies for music recommendations. This technique recommends songs with similar acoustic characteristics or meta-information (like composer, theme, style . . . ).
Although the aforementioned techniques have shown some degree of effectiveness in practice, however, most conventional techniques for generating music recommendations operate in a passive mode. For example, such passive techniques require consumers to first log on some portal sites and then take some actions to get suggestions. In other words, these recommendation services are passive and need to be triggered by users.
As described herein, various exemplary methods, devices, systems, etc., generate music recommendations and optionally buying options for consumers. Various exemplary techniques operate actively to enhance user experience, especially when applied to Web browsing.